Person Re-Identification in Aerial Imagery

Zhang, Qi; Yang, Yuxiang; Wei, Xing; Wang, Peng; Jiao, Bingliang; Zhang, Yanning

doi:10.1109/tmm.2020.2977528

Cited by 68 publications

(42 citation statements)

References 56 publications

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“…The authors in Reference [35] proposed a hybrid approach by employing both feature selection and feature extraction techniques for dimension reduction. Lately, a few authors, e.g., References [36,37], proposed feature extraction methods using the subspace pooling technique. The technique proposed in Reference [36] employed singular value decomposition (SVD) for subspace pooling to obtain the optimal set of features from high dimensional data.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…The authors in Reference [38] extracted a set of features using SVD and the principal singular vectors to encode the feature representation of input data. Zhang et al [37] also employed SVD for subspace pooling technique in their work. Guyon et al [39] have discussed multiple methods of feature selection in their research and concluded that clustering and matrix factorization performed best when the dimensions became very large.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Amjad

Khan

Nisar

et al. 2021

Sensors

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Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Background and Literature Reviewmentioning

confidence: 99%

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Amjad

Khan

Nisar

et al. 2021

Sensors

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“…UAV-Based Human Behavior Understanding Datasets. Thanks to the flexibility, UAVs have been used in many scenarios where ground cameras may be difficult to be deployed, and some UAV-based benchmarks [26,3,26,39,1,2,25,22] have been introduced for human behavior understanding. However, to the best of our knowledge, all the existing benchmarks have limitations with regard to the dataset size, the diversities of scenes, the provided task categories, and captured data modality types, etc.…”

Section: Related Workmentioning

confidence: 99%

UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

Zhang

et al. 2021

Preprint

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Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models. However, existing benchmarks have limitations in terms of the amount of captured data, types of data modalities, categories of provided tasks, and diversities of subjects and environments. Here we propose a new benchmark -UAV-Human -for human behavior understanding with UAVs, which contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. Our dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and nighttime over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. Such a comprehensive and challenging benchmark shall be able to promote the research of UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition. Furthermore, we propose a fisheye-based action recognition method that mitigates the distortions in fisheye videos via learning unbounded transformations guided by flat RGB videos. Experiments show the efficacy of our method on the UAV-Human dataset. The project page: https://github.com/SUTDCV/UAV-Human.

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“…Appearance based-Identifying people from their silhouettes can be approached as a re-identification (ReID) problem [18][19][20]. The vast majority of the literature on person ReID makes use of RGB images, as detailed in the review from Bedagkar-Gala et al [21] and the more recent deep-learning review from Wu et al [22].…”

Section: Reid From Imagesmentioning

confidence: 99%

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Masullo

Burghardt

Damen

et al. 2020

Sensors

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The use of visual sensors for monitoring people in their living environments is critical in processing more accurate health measurements, but their use is undermined by the issue of privacy. Silhouettes, generated from RGB video, can help towards alleviating the issue of privacy to some considerable degree. However, the use of silhouettes would make it rather complex to discriminate between different subjects, preventing a subject-tailored analysis of the data within a free-living, multi-occupancy home. This limitation can be overcome with a strategic fusion of sensors that involves wearable accelerometer devices, which can be used in conjunction with the silhouette video data, to match video clips to a specific patient being monitored. The proposed method simultaneously solves the problem of Person ReID using silhouettes and enables home monitoring systems to employ sensor fusion techniques for data analysis. We develop a multimodal deep-learning detection framework that maps short video clips and accelerations into a latent space where the Euclidean distance can be measured to match video and acceleration streams. We train our method on the SPHERE Calorie Dataset, for which we show an average area under the ROC curve of 76.3% and an assignment accuracy of 77.4%. In addition, we propose a novel triplet loss for which we demonstrate improving performances and convergence speed.

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Person Re-Identification in Aerial Imagery

Cited by 68 publications

References 56 publications

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

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